Real Time Weed Detection - CiteSeerX

REAL-TIME MACHINE VISION WEED-SENSING B.L. Steward and L. F. Tian Department of Agricultural Engineering University of Illinois at Urbana-Champaign Urbana, IL 61801

ABSTRACT Much work has been done to employ machine vision technology to sense weeds in crop fields. However, the use of machine vision weed-sensing with real-time objectives under variable outdoor lighting conditions is a relatively new area. This paper documents an effort to develop real-time weed sensing technologies using machine vision under variable lighting conditions. Images were acquired of weeds between rows of soybeans. Unsupervised learning by cluster analysis was used to classify pixels according to their color. This classified data was then used to train a Bayes classifier which was used to create a look-up table for real-time segmentation. Adaptive scanning with embedded segmentation was used to estimate weed population. These estimates were compared with manual weeds counts. The elapsed time to do this processing was measured to see if the real-time requirements were met. INTRODUCTION Large amounts of pesticides are applied each year to the fields of U.S. farmers. In 1989, about 800 million pounds of pesticides were applied (Aspelin et al., 1991). Herbicides make up a large component of the pesticides used and are relied on heavily for effective weed control resulting in reduced yield loss in grain production. Nevertheless, they represent a costly input to farmers and a source of ground water contamination. Typically, herbicides are applied with a blanket treatment to a whole field without regard to the spatial variability of the weeds in the field. This practice results in some areas where no or few weeds exist receiving just as much chemicals as those areas with high densities of weeds. Obviously, if a more sophisticated chemical delivery system was developed which applied chemicals where weeds existed and abstained where there were no weeds, chemical usage would be reduced and chemicals would be more effectively placed. Moreover, if chemical application rates could be varied according to local weed infestation conditions, further efficiency gains could be realized. These practices would result in lower environmental loading and increased profitability in the agricultural production sector. Selective spraying, spot spraying, or intermittent spraying are different names which are attached to this herbicide application method. Research has shown that weed aggregation exists (Marshall, 1988; Wilson and Brain, 1991; Thorton et al., 1990; Wiles, 1992; Cardina, 1995; Mortensen et al.,1993) and that if herbicides were applied in a spatially varying manner based on weed density, a reduction in herbicide usage would occur (Mortensen et al., 1995; Johnson, et al.., 1995). Therefore, several researchers have been working to develop engineering solutions to the technological challenges that a viable selective sprayer presents. A primary challenge is the sensing of weeds and discrimination of weeds from crop plants, crop residue and soil. Until now, this is the area where

2 most of the research effort has been directed. One approach to this problem has been to use of photodetectors to sense weeds (Hooper et al., 1976; Hagger et al., 1983; Shearer and Jones, 1991; Shropshire, 1990). While photodetector weed sensing represents a viable approach with commercial potential (Felton et al., 1991; Cooke, 1996; Beck, 1996 ), photodetectors have quite low spatial resolution (Nitch, 1991; Von Bargen et al., 1992) causing difficulty in distinguishing between types of plants except by using several narrow wavelength bands (Vrindts and DeBaerdemaeker, 1996; Shropshire, 1992). Machine vision has been used successfully for many applications in agriculture and shows great promise for weed sensing because it utilizes not only spectral information, but also spatial and textural information because of its use of high spatial resolution sensors. In addition, a large body of knowledge about machine vision and image processing techniques exists. In the area of weed sensing, much work has been done. Plant identification has been accomplished with the use of shape features of plants (Guyer et al., 1986; Franz et al., 1991a; Guyer et al., 1993; Bezenek, 1994; Woebbecke et al., 1992, 1995; Zhang and Chaisattapagon, 1995), textural information from images of crop canopy and plant leaves (Shearer and Holmes, 1990; Dave and Runtz, 1995; Zhang and Chaisattapagon, 1995), spectral information (Franz et al., 1991b; Zhang and Chaisattapagon, 1995) and fractal analysis on leaf shapes (Critten, 1996; Dave and Runtz, 1995). Shropshire (1989) used the Fourier and Hadamard transforms to detect if an image of the area between two rows of crop contained weeds. Andreasen et al. (1997) developed an algorithm to assess weed density. However, little research with a primary objective of real-time operation has been done on machine vision weed sensing. Real-time computation "must produce a correct result within in a specified time" (Lin and Burke, 1992) and is where "the correctness of a computation depends not only on the logical correctness but also on the time at which the results are produced" (Shin and Ramanathan, 1994). Tian et al. (1997a) commented on the use of lookup tables (LUT) for realtime image segmentation and object classification in their work on the identification of tomato seedlings for automated weed control. Nevertheless, real-time operation was never stated as a research objective. Only one paper was found that clearly set out to develop machine vision weed sensing algorithms "with a real-time implementation in mind" (Brivot and Marchant, 1996). They developed segmentation algorithms to separate plants, weeds, and soil in infrared images. The majority of the literature, however, describes the use of rather sophisticated algorithms without much regard for the real-time operation of such algorithms. Zhang and Chaisattapagon (1995) reveal this philosophy when making a comment about their research approach, "However, this approach . . . may require a long computation time. For real-time detection, this problem will need to be addressed." Similarly, much of the machine vision weed sensing research has been done with controlled lighting rather than variable natural lighting associated with outdoor field conditions. Most of the work in outdoor lighting conditions has been associated with robotic fruit harvesting (Sites and Delwiche, 1988; Slaughter and Harrell, 1989; Pla et al, 1993). Nevertheless, Brivot and Marchant (1996) and Tian et al. (1997a) documented successful sensing of weeds in diffuse natural lighting conditions. Using images formed with sunlight as an illumination source represents a greater challenge than using those created with controlled illumination. Tian (1995) pointed out that the level of

3 illumination under outdoor lighting conditions varies greatly. Moreover, direct sunlight causes substantial intensity differences within the images from the low intensity of shadows to the high intensity of reflections of shiny leaf surfaces. Furthermore, the temperature of daylight is variable causing the color of objects to change. Tian (1995) addressed these issues by using a light diffuser to minimize intensity differences within the image. In addition, an environmentally adaptive segmentation algorithm (EASA) was used to overcome light variation. In this research, real-time operation of a machine vision weed-sensing system was one of the primary objectives. OBJECTIVES As part of the Smart Sprayer Project, an effort to integrate real-time machine vision sensing, knowledge-based machine decision making, and variable rate nozzle technology, this research had the following objectives: 1) To develop an algorithm which segments the inter-row area of soybean field images and estimates the number of weeds in real-time under outdoor field lighting conditions. 2) To compare these estimates with manual weed counts. METHODS Data Collection A Sony XC-003 3-CCD camera was used to acquire images of the area between two rows of soybeans. The camera was mounted at a height of 3.35 m (11 ft) on a custom-made camera boom (Figure 1) for a Patriot XL sprayer (Tyler Industries, Benson, MN). A Computar model M6Z 1212 12.5-75mm F1.2 zoom lens was used to image an area 1.1 m (43 in.) by 0.8 m (33 in.) with the 12.5 mm zoom setting. Images were taken with the aperture set at F 8 for sunny conditions and F 5.6 for cloudy conditions, shutter speed at 1/250 s. The color temperature was set at 5600 K with manual white balance with the -2 db blue channel gain and a -20 db red channel gain. The camera had a resolution of 768 by 494 pixels. The Y/C output of the camera was routed to an Imagenation (Beaverton, OR) PXC200 color frame grabber which resided in a Pentium-based portable computer. The frame grabber had a resolution of 640 by 486 pixels. Each pixel corresponded to an area of approximately 0.002 by 0.002 m. The images were grabbed when the computer was triggered by the user and written to Windows bitmap files. Images were taken while the sprayer was moving with a forward travel speed of 0.6 km/hour (0.4 miles/hour) to minimize motion effects. The soybeans were planted in 0.76 m (30 in.) rows and the camera was positioned over the center of the area between two rows (Figure 2). Every 1.8 m (6 ft) plastic construction tape was placed across area between two rows. Forty of these imaging areas were used in this study from a transect 240 ft long in a test plot at the Agricultural Engineering Research Farm, Urbana, IL. The soybeans were approximately 0.13 m (5 in.) high when the images were taken. The area had a PPI treatment of 0.5 lb/ac Sencor 75DF for broadleaf control prior to planting. Three or

4 four images were required to cover the area between these tapes and were manually mosaiced to form one image of the area between the tapes. Weed counts were made manually by dividing the area between each pair of construction tapes in half and counting separately the number of broadleaf weeds and grasses in each half. These categories were further subdivided into small and large depending on whether the weed under consideration could fit inside a 0.025 m (1 in.) diameter metal ring.

Algorithm Development: Segmentation Segmentation is the division of an image into regions which have similar characteristics. In the case of weed sensing, there is an interest of dividing the image into regions which are plants (either weeds or crop) and background (soil, rocks and residue). For this research, a color sensor was used to provide a three component (RGB) data vector to describe each pixel. In earlier research (Tian, L. et al., 1997b), a single channel near-infrared (NIR) sensor which produced a one element data vector for each pixel was used. Since the NIR reflectance of plants is similar to that of residue (Woolley, 1971), the images produced by this sensor were difficult to segment by intensity. Residue could be removed by spatial analysis, but this additional analysis came at an added computational cost and was not feasible for real-time operation. A color RGB sensor was used in this research because of its commercial availability and because it collects a three element data vector for each pixel giving more information for segmentation. Tian (1995) used an environmentally adaptive segmentation algorithm (EASA) to segment plant regions and background regions in a color image with a goal of identifying and locating the centers of the tomato plants. The EASA was used for this research to segment plants from the background of the color images in real-time. The five steps to the EASA. are: 1. Data transformation to nullify intensity variation. 2. K-means cluster analysis for grouping pixels with similar color. 3. Bayes Classifier training. 4. Look-up Table creation. 5. Real-time segmentation. The first four steps calibrate the system for a particular lighting condition and are performed off-line making computation time less of a constraint. The last step is performed during real-time. The strategy here is to push the bulk of the computation to an offline calibration procedure so that during real-time operation, little computation is needed to segment the image. Representative images for particular lighting conditions are used to calibrate the system. Then, as changes in the lighting conditions are sensed during real-time operation, the system can quickly adapt to these changes by bringing in the pre-processed calibration data for that particular lighting condition. Further work is needed to determine the number of calibration points needed to cover the range of lighting conditions and how to determine if an image is representative of a particular lighting condition. The first step is to transform the data of a representative image into a form which minimizes the effect of intensity variation on the segmentation. This is necessary because

5 luminance in images captured under outdoor lighting conditions varies greatly across the image resulting in regions with similar color content with very different levels of brightness. An example of this would be a long corn leaf which has part of the leaf directly reflecting sunlight and part in the shadow of another plant. The whole leaf should be segmented as a plant region; however, if intensity is included in the data used for segmentation, it will dominate and will cause regions of similar intensity values to be grouped together. Figure 3 shows how intensity dominates the scatter in the pixel data as the pixel tend to form a cigar-shaped region along the intensity axis. To minimize the effect of intensity (luminance) variation, two data transformation techniques were explored. The first transformation employed the well-known nonlinear transformation from the RGB components to the normalized chromaticity coordinates. This transformation is defined as:

r'

R R%G%B

(1)

g'

G R%G%B

(2)

b'

B R%G%B

(3)

Since r+g+b = 1, this transformation maps all of the RGB color space onto a plane which forms edges of the color triangle as it intersects the R = 0, G = 0, and B =0 planes. This transformation removes the intensity information from the data and reduces the dimension of the color space as the RGB color space has a dimension of three, but the chromaticity coordinates have a dimension of 2 because of the affine linear dependent relationship listed above. The second transformation involved a change in coordinates from the RGB coordinate system to a coordinate system called the IV1V2 coordinate system described by: & V1

1 2

1 2

1 1 & V2 ' & 6 6 I 1 1 3

3

0 R 2 G 6 B 1 3

(4)

6 This transformation was motivated by and is similar to the initial linear transformation used before the nonlinear transformation of V1 and V2 to hue and saturation in the IHS system as described by Pratt (1991). This transformation is a rotation of the RGB coordinate vectors to an orientation such that one coordinate, I, is collinear with the intensity axis and the other two span a plane which is parallel to the color triangle. One of these “color” axes, V1 , is parallel to the side of the color triangle which goes from red to green. The value of the coefficient associated with the V1 axis gives a measure of redness or greenness, with red dominated pixels represented by negative coefficients and green dominated pixels represented by positive coefficients. A zero V1 coefficient represents a color with equal amounts of red and green. The other “color”axis, V2, is orthogonal to both I and V1 and runs in the direction from the origin to saturated blue on the color triangle (Figure 4). This transformation separates the intensity information from the color information and allows for further analysis based only on color by using the V1 and V2 coefficients and disregarding the I coordinate. The use of these two types of transformations was then compared qualitatively. After transforming the data, K-means cluster analysis was used to associate individual pixels with classes or clusters of similar pixels based on their color characteristics. This step of unsupervised learning reveals the underlying color distribution in the data. Four clusters were used to classify the data; two clusters typically corresponded to areas which could be considered to be background and the other two clusters to plant regions. The initial seed points were set at fixed values that were chosen after considering that two of the clusters would have a larger green content, and the other two would probably be more grey. After cluster analysis, each pixel was associated with a particular cluster or class. This information makes supervised learning possible, and a Bayes classifier was therefore trained and discriminant functions determined. To do this, the user must first input the clusters which he considers to be plants by visually comparing the original image and an image painted according to the cluster classification. It seems desirable to eliminate the need for user input and to have the system make this decision. This is an area to be explored in future work. At this point, it was possible to go through every possible color combination in RGB space and map each combination into class space as either a plant or background class. By doing this, a look-up table (LUT) was generated for real-time segmentation. When doing the real-time segmentation, the computer simply used the color combination as an index into the LUT and determined if particular pixels were either plants or background. Algorithm Development: Adaptive Scanning Because real-time operation was an objective, an estimate of the number of weeds between the rows of crop was desired without a great computational burden. No attempt was made at this point to distinguish individual crop plants from individual weeds, but an area of interest in each mosaiced image was selected manually to be analyzed for weed counting. Spatial features of the rows can be used to distinguish between crop row areas and inter-row areas, but were not used in this algorithm so as to eliminate errors associated with this type classification and to focus on the scanning results. Initially, object counting by 4-connectivity was used to count the number of segmented "plant" objects. However, it was found that such an algorithm required too much computation to

7 be feasible for real-time operation. Therefore, an algorithm was developed which uses a technique called "adaptive scanning." Adaptive scanning involves the scanning of image columns with a step size that is equal to the average run length of the objects encountered. This algorithm only considers connectivity in the direction of scanning. Some filtering was done to compensate for segmentation noise. This filtering took the form of allowing up to four “background” pixels to occur between two “plant” pixels before assigning the two “plant” pixels to two objects. Objects with less then three pixels in a runlength were considered to be noise and not an actual object. This algorithm was based on the idea that if the weeds have a compact shape, are randomly scattered in an image, and have small size variation within the area of an image, then the step size which minimizes the multiple counts of a weed and the misses of weeds would be equal to the average runlength across a weed. As the algorithm scans an image, it calculates the average runlength and then updates the step size for scanning of the next image to a weighted average of the past step size and the current average runlength. To measure the results of the segmentation and the adaptive scanning, a series of 40 images corresponding to 80 areas between rows of soybeans which had been manually sampled were analyzed. The estimates of the number of weeds were compared with the manual count. Each image was loaded into the computer memory. The area between the rows was divided into two parts, each of which corresponded to an area for which a manual weed count existed. The clock time at the beginning and ending of processing was stored so that the elapsed time to segment and scan each image could be calculated. The algorithm ran on a Dolch MegaPac with a Pentium processor operating at 133 MHz operating under a Windows 95 operating system. RESULTS AND DISCUSSION The segmentation of images revealed that the data transformation from RGB coordinates to the IV1V2 coordinate system resulted in segmentation with less noise then that of images where the data was transformed to chromaticity coordinates (RGB-rgb) before segmentation (Figure 5). In particular, in images taken in diffuse (cloudy) lighting conditions, cracks in the soil which were darker then the surrounding soil often segmented as plant regions with the RGB-rgb transformed data, but only isolated pixels of the cracks segmented as cracks with the RGB-IV1V2 transformed data. This makes sense when considering RGB coefficients (0,1,0) and (0,100,0) are both transformed to a the same chromaticity coordinates (0,1,0). However, (0,1,0) should be considered grey instead saturated green. This type of relationship is preserved with the RGBIV1V2 transformation of low intensity values. In images with direct lighting, regions which should have been classified as plants, contained many holes (pixels segmented as background) within the region (Figure 6) when segmenting RGB-rgb transformed data. In addition, the RGB-IV1V2 transformation gave noticeably faster results than the non-linear transform when going through the clustering and LUT building steps of the EASA. The weed population estimate from the adaptive scanning algorithm was highly correlated with the total manual count of the broadleaf weeds and grasses with a correlation coefficient of 0.94 (Figure 7). The small broadleaves category had the highest correlation coefficient (0.94) of all the other categories. This was probably caused by the range of small broadleaf populations

8 being much greater than that of the other categories. The correlation coefficients between large and small grasses were negative and with magnitude less than 0.3. Two additional reasons why the grasses did not correlate with the estimate are 1) they tended to have an elongated shape and 2) because their long thin leaves, they tended to not segment as plant regions. Other reasons for estimation error could be accounted for by: 1) multiple counts due to large leaves of weed being counted separately as large variation in weed size occurs and 2) weeds that were not very compact being counted several times. The mean elapsed time of the segmentation and scanning of an area 0.91 m (36 in.) by 0.76 m (30 in.) was 0.034 s with a standard deviation of 0.011s . This variability was due in part to the algorithm design since as the step size is varied, the time required to segment and scan the image will vary. Elapsed time variability could also be explained by the fact that the Windows 95 operating system is multitasking and not a real-time operation system. The small elapsed time for the algorithm indicates that increased complexity could be added to the algorithm for improved performance without jeopardizing real-time operation. If, for example, a sprayer had a camera mounted 2.74 m (9 ft) ahead of the spray nozzles and an image was acquired which had a 1.8 m (6 ft) vertical field of view, then at a travel speed of 16.1 km/hour (10 miles/hour), a decision would have to be executed 0.41 s after an image was captured. This algorithm’s elapsed time falls well below this amount even when multiple areas between rows are considered. CONCLUSION An algorithm which employs environmentally adaptive segmentation and adaptive scanning was developed and implemented. The RGB-IV1V2 data transformation resulted in qualitatively superior segmentation results over the RGB-rgb transformation. Adaptive scanning produced weed population estimates which were highly correlated with total weed counts. The low elapsed computation time shows that real-time machine vision weed detection is viable and allows room for more sophisticated scanning algorithms which require more computation time. ACKNOWLEDGMENTS The authors would like to acknowledge and thank the USDA National Needs Fellowship program, the Illinois Council on Food and Agricultural Research, and Tyler Industries for their support of this research. The use of trade names is only meant to provide specific information to the reader, and does not constitute endorsement by the University of Illinois. REFERENCES Andreason, C., M. Rudemo, and S. Sevestre. 1997. Assessment of weed density at an early stage by use of image processing. Weed Research. 37(1):5-18.

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